In this paper we provide a method for evaluating interest point detectors independently of image descriptors. This is possible because we have compiled a unique data set enabling us to determine if common interest points are found. The data contains 60 scenes of a wide range of object types, and for each scene we have 119 precisely located camera positions obtained from a camera mounted on an industrial robot arm. The scene surfaces have been scanned using structured light, providing precise 3D ground truth. We have investigated a number of the most popular interest point detectors. This is done in relation to the number of interest points, the recall rate as a function of camera position and light variation, and the sensitivity relative to model parameter change. The overall conclusion is that the Harris corner detector has a very high recall rate, but is sensitive to change in scale. The Hessian corners perform overall well followed by MSER (Maximally Stable Extremal Regions), whereas the FAST corner detector, IBR (Intensity Based Regions) and EBR (Edge Based Regions) performs poorly. Furthermore, the repeatability of the corner detectors is quite unaffected by the parameter setting, and only the number of interest points change.